AUTOMATICALLY PRESCRIBING TOTAL BUDGET FOR MARKETING AND SALES RESOURCES AND ALLOCATION ACROSS SPENDING CATEGORIES
A facility for automatically prescribing, for a distinguished offering, an allocation of resources to a total marketing budget and/or individual marketing activities is described.
The described technology is directed to the field of automated decision support tools, and, more particularly, to the field of automated budgeting tools.
BACKGROUNDMarketing communication (“marketing”) is the process by which the sellers of a product or a service—i.e., an “offering”—educate potential purchasers about the offering. Marketing is often a major expense for sellers, and is often made of a large number of components or categories, such as a variety of different advertising media and/or outlets, as well as other marketing techniques. Despite the complexity involved in developing a marketing budget attributing a level of spending to each of a number of components, few useful automated decision support tools exists, making it common to perform this activity manually, relying on subjective conclusions, and in many cases producing disadvantageous results.
In the few cases where useful decision support tools exist, it is typically necessary for the tool's user to provide large quantities of data about past allocations of marketing resources to the subject offering, and the results that that they produced.
The inventors have recognized that, in many cases, such as in the case of a new offering, the large quantities of data about past allocations of marketing resources to the subject offering and the results that that they produced that a user would have to provide to a conventional decision support tool is not available. The inventors have further recognized that, even where such data is available, it can be inconvenient to access this data and provide it to the decision support tool.
Accordingly, a tool that automatically prescribed an advantageous allocation of funds or other resources to an offering and its various components without requiring the user to provide historical performance data for the offering would have significant utility.
A software facility that uses a qualitative description of a subject offering to automatically prescribe both (1) a total budget for marketing and sales resources for a subject offering and (2) an allocation of that total budget over multiple spending categories—also referred to as “activities”—in a manner intended to optimize a business outcome such as profit for the subject offering based on experimentally-obtained econometric data (“the facility”) is provided.
In an initialization phase, the facility considers data about historical marketing efforts for various offerings that have no necessary relationship to the marketing effort for the subject offering. The data reflects, for each such effort: (1) characteristics of the marketed offering; (2) total marketing budget; (3) allocation among marketing activities; and (4) business results. This data can be obtained in a variety of ways, such as by directly conducting marketing studies, harvesting from academic publications, etc.
The facility uses this data to create resources adapted to the facility's objectives. First, the facility calculates an average elasticity measure for total marketing budget across all of the historical marketing efforts that predicts the impact on business outcome of allocating a particular level of resources to total marketing budget. Second, the facility derives a number of adjustment factors for the average elasticity measure for total marketing budget that specify how much the average elasticity measure for total marketing budget is to be increased or decreased to reflect particular characteristics of the historical marketing efforts. Third, for the historical marketing efforts of each of a number groups of qualitatively similar offerings, the facility derives per-activity elasticity measures indicating the extent to which each marketing activity impacted business outcome for marketing efforts for the group.
The facility uses interviewing techniques to solicit a qualitative description of the subject offering from user. The facility uses portions of the solicited qualitative description to identify adjustment factors to apply to the average elasticity measure for total marketing budget. The facility uses a version of average elasticity measure for total marketing budget adjusted by the identified adjustment factors to identify an ideal total marketing budget expected to produce the highest level of profit for the subject offering, or to maximize some other objective specified by the user.
After identifying the ideal total marketing budget, the facility uses the solicited qualitative description of the subject offering to determine which of the groups of other offerings the subject offering most closely matches, and derives a set of ideal marketing activity allocations from the set of per-activity elasticity measures derived for that group.
In this manner, the facility automatically prescribes a total marketing resource allocation and distribution for the subject offering without requiring the user to provide historical performance data for the subject offering.
While various embodiments are described in terms of the environment described above, those skilled in the art will appreciate that the facility may be implemented in a variety of other environments including a single, monolithic computer system, as well as various other combinations of computer systems or similar devices connected in various ways. In various embodiments, a variety of computing systems or other different client devices may be used in place of the web client computer systems, such as mobile phones, personal digital assistants, televisions, cameras, etc.
In order to define the profit curve and identify the total marketing budget level at which it reaches its peak, the facility first determines a total marketing budget elasticity appropriate for the subject offering. This elasticity value falls in a range between 0.01 and 0.30, and is overridden to remain within this range. The facility calculates the elasticity by adjusting an initial elasticity value, such as 0.10 or 0.11, in accordance with a number of adjustment factors each tied to a particular attribute value for the subject offering. Sample values for these adjustment factors are shown below in Table 1.
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The facility then uses the adjusted total marketing budget elasticity to determine the level of total marketing budget at which the maximum profit occurs, as is discussed in detail below in Table 2.
It will be appreciated by those skilled in the art that the above-described facility may be straightforwardly adapted or extended in various ways. While the foregoing description makes reference to particular embodiments, the scope of the invention is defined solely by the claims that follow and the elements explicitly recited therein.
Claims
1. A method in a computing system for automatically prescribing an allocation of resources to a total marketing budget for a distinguished offering, with the goal of optimizing a distinguished business outcome for the offering that is expected to be driven at least in part by the allocation of resources to the total marketing budget, comprising:
- receiving qualitative attributes of the distinguished offering from a user;
- retrieving an experimentally-obtained average total marketing budget lift factor;
- adjusting the experimentally-obtained average total marketing budget lift factor based upon at least two of the received qualitative attributes of the distinguished offering; and
- using the adjusted experimentally-obtained average total marketing budget lift factor to determine an allocation of resources to a total marketing budget that tends to optimize the distinguished business outcome.
2. The method of claim 1, further comprising persistently storing the determined allocation of resources.
3. The method of claim 1, further comprising displaying the determined allocation of resources to a user.
4. A computer-readable medium whose contents cause a computing system to perform a method for automatically prescribing an allocation of resources to a total marketing budget for a distinguished offering, with the goal of optimizing a distinguished business outcome for the offering that is expected to be driven at least in part by the allocation of resources to the total marketing budget, comprising:
- receiving qualitative attributes of the distinguished offering from a user;
- retrieving an experimentally-obtained average total marketing budget lift factor;
- adjusting the experimentally-obtained average total marketing budget lift factor based upon at least two of the received qualitative attributes of the distinguished offering; and
- using the adjusted experimentally-obtained average total marketing budget lift factor to determine an allocation of resources to a total marketing budget that tends to optimize the distinguished business outcome.
5. A method in a computing system for automatically prescribing an allocation of resources to each of one or more activities to be performed with respect to a distinguished offering, with the goal of optimizing a business outcome for the offering that is expected to be driven at least in part by the activities, comprising:
- receiving information from a user characterizing attributes of the distinguished offering;
- for each of the activities, determining a lift factor derived from experimental results for one or more offerings that, while distinct from the distinguished offerings, are determined to be similar to the distinguished offerings based on the received information characterizing attributes of the distinguished offering, the lift factor indicating the predicted effect of the activity on the business outcome; and
- using the retrieved lift factors to generate an allocation of resources for each of the activities.
6. The method of claim 5 wherein the determining comprises:
- using the received information characterizing a first portion of the attributes of the distinguished offering to select a lift factor corresponding to experimental results for offerings whose first portion of attributes are characterized in a similar way; and
- adjusting the selected lift factor based on using the received information characterizing a second portion of the attributes of the distinguished offering.
7. The method of claim 5, further comprising automatically committing resources to at least one of the activities in accordance with the allocation generated for those activities.
8. A computer-readable medium whose contents cause a computing system to perform a method for automatically prescribing an allocation of resources to each of one or more activities to be performed with respect to a distinguished offering, with the goal of optimizing a business outcome for the offering that is expected to be driven at least in part by the activities, the method comprising:
- receiving information from a user characterizing attributes of the distinguished offering;
- for each of the activities, determining a lift factor derived from experimental results for one or more offerings that, while distinct from the distinguished offerings, are determined to be similar to the distinguished offerings based on the received information characterizing attributes of the distinguished offering, the lift factor indicating the predicted effect of the activity on the business outcome; and
- using the retrieved elasticity measures to generate an allocation of resources for each of the activities.
9. The computer-readable medium of claim 8 wherein the determining comprises:
- using the received information characterizing a first portion of the attributes of the distinguished offering to select a lift factor corresponding to experimental results for offerings whose first portion of attributes are characterized in a similar way; and
- adjusting the selected lift factor based on using the received information characterizing a second portion of the attributes of the distinguished offering.
10. The computer-readable medium of claim 8 further comprising automatically committing resources to at least one of the activities in accordance with the allocation generated for those activities.
11. One or more computer memories collectively storing a generalized marketing lift factor data structure, comprising a plurality of entries each for a different business offering profile, each business offering profile describing a group of one or more business offerings that are qualitatively distinguished from groups of business offerings of the other business offering profile, each entry containing a lift factor indicating the effect of a marketing activity with respect to the group of business offerings on a business outcome, such that, for a distinguished business offering described by a distinguished one of the profiles, the lift factor indicated by the distinguished entry may be used to automatically specify an allocation of marketing resources to the distinguished business offering.
Type: Application
Filed: Apr 3, 2015
Publication Date: Dec 10, 2015
Inventors: David Cavander (Los Angeles, CA), Wes Nichols (Los Angeles, CA), Jon Vein (Los Angeles, CA), Dominique Hanssens (Los Angeles, CA)
Application Number: 14/678,793